Author
Listed:
- PETAR JEVTIĆ
(School of Mathematical and Statistical Sciences, Arizona State University, 900 Palm Walk, Tempe, AZ 85281, USA)
- NICOLAS LANCHIER
(School of Mathematical and Statistical Sciences, Arizona State University, 900 Palm Walk, Tempe, AZ 85281, USA)
Abstract
Smart contract risk can be defined as a financial risk of loss due to cyber attacks on or contagious failures of smart contracts. Its quantification is of paramount importance to technology platform providers as well as companies and individuals when considering the deployment of this new technology. That is why, as our primary contribution, we propose a structural framework of aggregate loss distribution for smart contract risk under the assumption of a tree-stars graph topology representing the network of interactions among smart contracts and their users. To our knowledge, there exist no theoretical frameworks or models of an aggregate loss distribution for smart contracts in this setting. To achieve our goal, we contextualize the problem in the probabilistic graph-theoretical framework using bond percolation models. We assume that the smart contract network topology is represented by a random tree graph of finite size, and that each smart contract is the center of a random star graph whose leaves represent the users of the smart contract. We allow for heterogeneous loss topology superimposed on this smart contract and user topology and provide analytical results and instructive numerical examples.
Suggested Citation
Petar Jevtiä† & Nicolas Lanchier, 2021.
"Probabilistic Framework For Loss Distribution Of Smart Contract Risk,"
Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 24(07n08), pages 1-31, December.
Handle:
RePEc:wsi:acsxxx:v:24:y:2021:i:07n08:n:s0219525921500144
DOI: 10.1142/S0219525921500144
Download full text from publisher
As the access to this document is restricted, you may want to
for a different version of it.
Corrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:wsi:acsxxx:v:24:y:2021:i:07n08:n:s0219525921500144. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
We have no bibliographic references for this item. You can help adding them by using this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Tai Tone Lim (email available below). General contact details of provider: http://www.worldscinet.com/acs/acs.shtml .
Please note that corrections may take a couple of weeks to filter through
the various RePEc services.